gov.noaa.nodc:0220087 eng utf8 dataset NOAA National Centers for Environmental Information ncei.info@noaa.gov https://www.ncei.noaa.gov/contact HTTPS NOAA Contact Information Information for contacts at NCEI. information custodian 2025-09-30T13:10:12Z ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) NCCOS spatial modeling of threatened Caribbean corals: process-based models for Acropora palmata (elkhorn coral) distributions in the U.S. Virgin Islands (NCEI Accession 0220087) 2020-11-02 publication 2020-09-26 creation International DOI Foundation (IDF) 10.25921/61by-cw46 NOAA National Centers for Environmental Information gov.noaa.nodc:0220087 NCEI Archive Management System 0220087 NOAA National Centers for Environmental Information ncei.info@noaa.gov https://www.ncei.noaa.gov/contact HTTPS NCEI Contact Information Information for contacts at NCEI. information publisher NOAA National Centers for Coastal Ocean Science (NCCOS) https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information resourceProvider Shay Viehman NOAA National Centers for Coastal Ocean Science (NCCOS) shay.viehman@noaa.gov https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information principalInvestigator Yi-Hsiu Chen yihsiu0402@gmail.com principalInvestigator NCCOS Data Manager nccos.data@noaa.gov pointOfContact Chen, Yi-Hsiu author Viehman, Shay NOAA National Centers for Coastal Ocean Science https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information author Shertzer, Kyle author Egan, Katharine author Groves, Sarah author NOAA National Centers for Coastal Ocean Science (NCCOS) https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information contributor tableDigital This dataset is a compilation of modeled current and future density distributions of threatened elkhorn corals Acropora palmata in the shallow water (bottom depth ≥ -20 m) off St. Thomas, St. John and St. Croix, U.S. Virgin Islands. The raster data sets contain predicted distributions of species density and the prediction uncertainty in 2013, 2014, 2015, 2035 and 2055 estimated using process-based random forest (RF) and dynamic range models (DRM). These predictions were generated to inform Caribbean A. palmata restoration plans in the U.S. Virgin Islands. With the recently expanded inclusion of coral species as Threatened under the U.S. Endangered Species Act (ESA), current and future predictions of coral population dynamics and species distributions are needed to enable effective species based management for conservation and to minimize potential threats to populations. This calls for consideration of both spatial and temporal dynamics in the coral populations in the development of predictive maps of species distributions are necessary. To this end, we apply process-based dynamic range models (DRMs) to jointly model the spatio-temporal population dynamics and spatial habitat suitability of Threatened elkhorn corals Acropora palmata in the U.S. Caribbean under a hierarchical Bayesian framework. The main objectives of this project were to predict: 1) current geospatial distribution range and abundance of the species, and 2) future distribution range and abundance based on a projected climate scenario. The purpose of these data is to: 1) identify priority outplant and monitoring area in USVIs, and 2) provide effective conservation guidance for restoration actions that target declining Acropora palmata population in the Caribbean. Funding Agency: US DOC; NOAA; NMFS; Office of Science and Technology (OST) Funding Agency: US DOC; NOAA; National Centers for Coastal Ocean Science (NCCOS) completed historicalArchive NOAA National Centers for Environmental Information ncei.info@noaa.gov https://www.ncei.noaa.gov/contact HTTPS NCEI Contact Information Information for contacts at NCEI. information pointOfContact asNeeded https://www.ncei.noaa.gov/access/metadata/landing-page/bin/gfx?id=gov.noaa.nodc:0220087 Preview graphic PNG 0220087 NCEI ACCESSION NUMBER 2020-09-24 publication CORAL theme NODC DATA TYPES THESAURUS LIDAR instrument NODC INSTRUMENT TYPES THESAURUS GIS product model output theme NODC OBSERVATION TYPES THESAURUS NOAA National Centers for Coastal Ocean Science dataCentre NODC COLLECTING INSTITUTION NAMES THESAURUS NOAA National Centers for Coastal Ocean Science dataCentre NODC SUBMITTING INSTITUTION NAMES THESAURUS Caribbean Sea place NODC SEA AREA NAMES THESAURUS oceanography theme WMO_CategoryCode 2012-09-15 publication DOC/NOAA/NOS/NCCOS > National Centers for Coastal Ocean Science, National Ocean Service, NOAA, U.S. Department of Commerce dataCentre Global Change Master Directory (GCMD) Data Center Keywords 2026 revision 24 Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA) Greenbelt MD https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords HTTPS GCMD Keyword Forum Page Global Change Master Directory (GCMD). 2025. GCMD Keywords, Version 21. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords information custodian NCCOS Project #180: National Coral Reef Monitoring Program Implementation: Biological and Socioeconomic Monitoring NCCOS Project #292: Spatial Predictive Modeling of Threatened ESA Corals in the U.S. Atlantic and Caribbean project NCCOS Project Names EARTH SCIENCE > SOLID EARTH > GEOMORPHIC LANDFORMS/PROCESSES > COASTAL LANDFORMS > CORAL REEFS theme Global Change Master Directory (GCMD) Science Keywords 2026 revision 24 Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA) Greenbelt MD https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords HTTPS GCMD Keyword Forum Page Global Change Master Directory (GCMD). 2025. GCMD Keywords, Version 21. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords information custodian Coastal Change > Climate Impacts on Ecosystems Marine Spatial Ecology > Ecological and Biogeographic Assessments theme NCCOS Research Areas Derived Data Product Geospatial Model theme NCCOS Research Data Types Acropora palmata Density Elkhorn corals Uncertainty theme Provider Keywords LIDAR > Light Detection and Ranging instrument Global Change Master Directory (GCMD) Instrument Keywords 2026 revision 24 Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA) Greenbelt MD https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords HTTPS GCMD Keyword Forum Page Global Change Master Directory (GCMD). 2025. GCMD Keywords, Version 21. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords information custodian Models/Analyses > Data Analysis > Environmental Modeling instrument Provider Instruments OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > CARIBBEAN SEA place Global Change Master Directory (GCMD) Location Keywords 2026 revision 24 Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA) Greenbelt MD https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords HTTPS GCMD Keyword Forum Page Global Change Master Directory (GCMD). 2025. GCMD Keywords, Version 21. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords information custodian U.S. States and Territories > U.S. Virgin Islands Waterbodies > Caribbean Sea place NCCOS Regions of Study Caribbean Sea St. Croix St. John St. Thomas U.S. Virgin Islands place Provider Place Names 47HBUC Send2NCEI Submission Package ID accessLevel: public otherRestrictions Cite as: Chen, Yi-Hsiu; Viehman, Shay; Shertzer, Kyle; Egan, Katharine; Groves, Sarah (2020). NCCOS spatial modeling of threatened Caribbean corals: process-based models for Acropora palmata (elkhorn coral) distributions in the U.S. Virgin Islands (NCEI Accession 0220087). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/61by-cw46. Accessed [date]. Distribution liability: NOAA and NCEI make no warranty, expressed or implied, regarding these data, nor does the fact of distribution constitute such a warranty. NOAA and NCEI cannot assume liability for any damages caused by any errors or omissions in these data. If appropriate, NCEI can only certify that the data it distributes are an authentic copy of the records that were accepted for inclusion in the NCEI archives. otherRestrictions Use liability: NOAA and NCEI cannot provide any warranty as to the accuracy, reliability, or completeness of furnished data. Users assume responsibility to determine the usability of these data. The user is responsible for the results of any application of this data for other than its intended purpose. Anselmi-Molina, C. M., Canals, M., Morell, J., Gonzalez, J., Capella, J., & Mercado, A. (2012). Development of an Operational Nearshore Wave Forecast System for Puerto Rico and the U.S. Virgin Islands. Journal of Coastal Research, 284, 1049–1056. https://doi.org/10.2112/jcoastres-d-11-00132.1 2012-09 publication International DOI Foundation (IDF) https://doi.org/10.2112/jcoastres-d-11-00132.1 https://doi.org/10.2112/jcoastres-d-11-00132.1 HTTPS https://doi.org/10.2112/jcoastres-d-11-00132.1 Cited Publication information Anselmi-Molina, Carlos M. author Canals, Miguel author Morell, Julio author Gonzalez, Juan author Capella, Jorge author Mercado, Aurelio author Coastal Education and Research Foundation publisher documentDigital Journal of Coastal Research 284 ISSN 0749-0208, ISSN 1551-5036, pp. 1049-1056 crossReference sciencePaper Appendini, C. M., Torres-Freyermuth, A., Salles, P., López-González, J., & Mendoza, E. T. (2014). Wave Climate and Trends for the Gulf of Mexico: A 30-Yr Wave Hindcast. Journal of Climate, 27(4), 1619–1632. https://doi.org/10.1175/jcli-d-13-00206.1 2014-02-10 publication International DOI Foundation (IDF) https://doi.org/10.1175/jcli-d-13-00206.1 https://doi.org/10.1175/jcli-d-13-00206.1 HTTPS https://doi.org/10.1175/jcli-d-13-00206.1 Cited Publication information Appendini, Christian M. Laboratorio de Ingeniería y Procesos Costeros, IINGEN–UNAM, Sisal, Mexico author Torres-Freyermuth, Alec Laboratorio de Ingeniería y Procesos Costeros, IINGEN–UNAM, Sisal, Mexico author Salles, Paulo Laboratorio de Ingeniería y Procesos Costeros, IINGEN–UNAM, Sisal, Mexico author López-González, Jose Laboratorio de Ingeniería y Procesos Costeros, IINGEN–UNAM, Sisal, Mexico author Mendoza, E. Tonatiuh Laboratorio de Ingeniería y Procesos Costeros, IINGEN–UNAM, Sisal, Mexico author American Meteorological Society publisher documentDigital Journal of Climate 27(4) ISSN 0894-8755, ISSN 1520-0442, pp. 1619-1632 crossReference sciencePaper Breiman, L. (2001). Machine Learning, 45(1), 5–32. https://doi.org/10.1023/a:1010933404324 2001 publication International DOI Foundation (IDF) https://doi.org/10.1023/a:1010933404324 https://doi.org/10.1023/a:1010933404324 HTTPS https://doi.org/10.1023/a:1010933404324 Cited Publication information Breiman, Leo author Springer Science and Business Media LLC publisher documentDigital Machine Learning 45(1) ISSN 0885-6125, pp. 5-32 crossReference sciencePaper Chen, Y., Shertzer, K. W., & Viehman, T. S. (2020). Spatio‐temporal dynamics of the threatened elkhorn coral Acropora palmata: Implications for conservation. Diversity and Distributions, 26(11), 1582–1597. https://doi.org/10.1111/ddi.13143 2020-08-29 publication International DOI Foundation (IDF) https://doi.org/10.1111/ddi.13143 https://doi.org/10.1111/ddi.13143 HTTPS https://doi.org/10.1111/ddi.13143 Cited Publication information Chen, Yi‐Hsiu National Academies of Sciences Engineering and Medicine National Research Council Washington DC USA author Shertzer, Kyle W. Southeast Fisheries Science Center National Marine Fisheries Service Beaufort NC USA author Viehman, T. Shay National Centers for Costal Ocean Science NOAA National Ocean Service Beaufort NC USA author Zhan, Aibin editor Wiley publisher documentDigital Diversity and Distributions 26(11) ISSN 1366-9516, ISSN 1472-4642, pp. 1582-1597 crossReference sciencePaper Kibler, S. R., Tester, P. A., Kunkel, K. E., Moore, S. K., & Litaker, R. W. (2015). Effects of ocean warming on growth and distribution of dinoflagellates associated with ciguatera fish poisoning in the Caribbean. Ecological Modelling, 316, 194–210. https://doi.org/10.1016/j.ecolmodel.2015.08.020 2015-11 publication International DOI Foundation (IDF) https://doi.org/10.1016/j.ecolmodel.2015.08.020 https://doi.org/10.1016/j.ecolmodel.2015.08.020 HTTPS https://doi.org/10.1016/j.ecolmodel.2015.08.020 Cited Publication information Kibler, Steven R. author Tester, Patricia A. author Kunkel, Kenneth E. author Moore, Stephanie K. author Litaker, R. Wayne author Elsevier BV publisher documentDigital Ecological Modelling 316 ISSN 0304-3800, pp. 194-210 crossReference sciencePaper Pagel, J., & Schurr, F. M. (2011). Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics. Global Ecology and Biogeography, 21(2), 293–304. https://doi.org/10.1111/j.1466-8238.2011.00663.x 2011-04-18 publication International DOI Foundation (IDF) https://doi.org/10.1111/j.1466-8238.2011.00663.x https://doi.org/10.1111/j.1466-8238.2011.00663.x HTTPS https://doi.org/10.1111/j.1466-8238.2011.00663.x Cited Publication information Pagel, Jörn author Schurr, Frank M. author Wiley publisher documentDigital Global Ecology and Biogeography 21(2) ISSN 1466-822X, ISSN 1466-8238, pp. 293-304 crossReference sciencePaper ter Braak, C. J. F., & Vrugt, J. A. (2008). Differential Evolution Markov Chain with snooker updater and fewer chains. Statistics and Computing, 18(4), 435–446. https://doi.org/10.1007/s11222-008-9104-9 2008-10-21 publication International DOI Foundation (IDF) https://doi.org/10.1007/s11222-008-9104-9 https://doi.org/10.1007/s11222-008-9104-9 HTTPS https://doi.org/10.1007/s11222-008-9104-9 Cited Publication information ter Braak, Cajo J. F. author Vrugt, Jasper A. author Springer Science and Business Media LLC publisher documentDigital Stat Comput 18(4) ISSN 0960-3174, ISSN 1573-1375, pp. 435-446 crossReference sciencePaper NASA Multi-scale Ultra-high Resolution Sea Surface Temperature 2020 publication https://podaac.jpl.nasa.gov/MEaSUREs-MUR?sections=about HTTPS NASA Multi-scale Ultra-high Resolution Sea Surface Temperature Data Source information crossReference NOAA NCEI Coastal Lidar Archive 2020 publication https://www.ngdc.noaa.gov/mgg/bathymetry/lidar.html HTTPS NOAA NCEI Coastal Lidar Archive Data Source information crossReference Spatial Predictive Modeling of Threatened ESA Corals in the U.S. Atlantic and Caribbean 2015 publication https://coastalscience.noaa.gov/project/spatial-predictive-modeling-threatened-esa-corals-u-s-atlantic-caribbean/ HTTPS https://coastalscience.noaa.gov/project/spatial-predictive-modeling-threatened-esa-corals-u-s-atlantic-caribbean/ National Centers for Coastal Ocean Science (NCCOS) project page information NOAA National Centers for Coastal Ocean Science (NCCOS) https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information publisher documentDigital crossReference project National Coral Reef Monitoring Program Implementation: Biological and Socioeconomic Monitoring 2013 publication https://coastalscience.noaa.gov/project/national-coral-reef-monitoring-program-biological-socioeconomic/ HTTPS https://coastalscience.noaa.gov/project/national-coral-reef-monitoring-program-biological-socioeconomic/ National Centers for Coastal Ocean Science (NCCOS) project page information NOAA National Centers for Coastal Ocean Science (NCCOS) https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information publisher documentDigital crossReference project eng; USA utf8 environment oceans imageryBaseMapsEarthCover biota -65.15859 -64.42007 17.62387 18.47301 2013-01-01 2055-12-31 Methods: For more details, see Chen et al., 2020. To jointly model the spatio-temporal population dynamics and spatial habitat suitability of elkhorn corals Acropora palmata, we adopted the general framework of dynamic range models (DRMs; Pagel and Schurr, 2012) in which the demographic rates were related with a niche model that describes the response of population growth rates to environmental predictors, which were bathymetric depth, mean summer sea surface temperature, and maximum significant wave height in this study. Bathymetric depth data were derived from high resolution remote sensing products including multibeam sonar and light detection and ranging (LiDAR; NOAA). Mean summer sea surface temperature data in June-August were derived from NASA Multi-Scale Ultra-High Resolution Sea Surface Temperature (MUR SST; NASA) and were downscaled from an original 0.01° spatial resolution to 50 m through bilinear interpolation. Maximum significant wave height data were forecasted by CariCOOS Nearshore Wave model, which was based on a Simulating Waves Nearshore (SWAN) model and validated by field observations from CARICOOS wave buoys (Anselmi-Molina et al., 2012). Wave data were downscaled from an original 1 km spatial resolution to 50 m through bilinear interpolation. For the future climate scenario (years from 2016 to 2055), we assumed SST and significant wave height will increase at a rate of 0.19 °C decade−1, estimated from eleven global climate models (GCMs) downscaled for eastern Caribbean Sea based on Representative Concentration Pathways (RCPs) 6.0 scenario (Kibler et al., 2015), and 0.03 m year-1, based on a 30 years wave hindcast from 1979 to 2008 in the Caribbean Sea (Appendini et al., 2014), respectively. As with DRM framework, the latent population process was constructed with a spatially explicit, grid-based model, in which the local colonies in each grid cell is associated with neighborhood cells via dispersal on distance-dependent rates. Because the size of coral colonies is related to their contributions to population growth, a size-structured population model was used in this analysis instead of the Ricker model proposed in the original DRMs. The model was constructed with a hierarchical Bayesian approach and consists of three components: process model, data model, and parameter model. DRMs were fitted to the data using Differential Evolution Markov Chain (DE-MCZS; ter Braak and Vrugt, 2008) sampler, an efficient adaptive Markov Chain Monte Carlo sampling (MCMC) method, with two independent parallel chains for 600,000 iteration steps each in total, in which first 350,000 samples were discarded as ‘burn-in.’ Convergence of the chains was assessed using the Gelman-Rubin diagnostic test and visual inspection of the chain histories. The initial abundance in each grid cell, which is required for dynamic range models, was estimated from correlative species distribution modeling method random forest (RF; Breiman, 2001) and quadratic multinomial logit model. For each of estimated parameters, 500 sets of posterior samples were drawn from the posterior distributions and used in the spatio-temporal simulations of population dynamics from 2014 to 2055. The mean and standard deviation of the 500 simulated predicted density distributions of each year between 2014 and 2055 were calculated for the final density predictions and uncertainty. Also included in this accession are supplemental rda data sets containing Bayesian posterior distributions (unitless) of each parameter estimated from 2013-2015 observations with Differential Evolution Markov Chain sampler, an efficient adaptive Markov Chain Monte Carlo sampling (MCMC) method, with two independent parallel chains for 600000 iteration steps each in total. This dataset was calculated and can be read in R using package ‘BayesianTools’. Processing and analysis methods are described in the reference publications. NOAA National Centers for Environmental Information +1-301-713-3277 ncei.info@noaa.gov pointOfContact In most cases, electronic downloads of the data are free. However, fees may apply for custom orders, data certifications, copies of analog materials, and data distribution on physical media. Contact NCEI for other distribution options and instructions. Geodatabase .GBD (and ancillary files: .atx, .freelist, .gdbindexes, .gdbtable, .gdbtablx, .spx GeoTIFF (.TIF and ancillary files: .tfw, .aux, .ovr) PDF JPEG 8.888 https://doi.org/10.25921/61by-cw46 HTTPS Web browser NCEI Dataset Landing Page Navigate directly to the URL for a descriptive web page with download links. information https://www.ncei.noaa.gov/archive/accession/oas/220087 HTTPS Web browser Descriptive Information Navigate directly to the URL for a descriptive web page with download links. information https://www.ncei.noaa.gov/archive/accession/download/220087 HTTPS Web browser HTTPS Navigate directly to the URL for data access and direct download. download ftp://ftp-oceans.ncei.noaa.gov/nodc/archive/arc0162/0220087/ FTP Any FTP client FTP These data are available through the File Transfer Protocol (FTP). FTP is no longer supported by most internet browsers. You may copy and paste the FTP link to the data into an FTP client (e.g., FileZilla or WinSCP). download dataset NCEI Accession 0220087 v1.1 was published. 2020-11-02T18:44:33Z NCEI Accession 0220087 v1.1 publication https://www.ncei.noaa.gov/archive/accession/0220087/1.1 HTTPS NCEI Accession 0220087 v1.1 published 2020-11-02T18:44:33Z download dataset Parameter or Variable: Density (calculated); Units: Colonies per 30 square meter plot; Observation Category: model output; Sampling Instrument: Models/Analyses > Data Analysis > Environmental Modeling; Sampling and Analyzing Method: Coral density in the initial year 2013 was estimated by the average of ten Random Forest predictions. Coral density of each size class were estimated by the average of 500 simulated current predicted (2014, 2015) or future projected (2035, 2055) spatial density distributions generated with 500 sets of posterior samples drawn from the posterior distributions of estimated parameters. The total density was a sum of colony counts in all four size classes. To better inform restoration and monitoring sites selection, predicted distribution of coral density in 2015 within potential habitats, defined as shallow water (depth ≤ 20 m) with habitable benthic habitat (including hardbottom, aggregate and patch coral reefs, pavements, bedrock, and scattered corals and rocks) were also provided. However, as future changes in benthic habitat is unclear, benthic habitat type was only applied to the last survey year 2015 and not included in the analysis for future projection. For more details, see Chen et al., 2020.; Data Quality Method: Predictive coral distribution maps in the initial years 2013 were evaluated by multiple evaluation methods, such as the Pearson correlation coefficient, root mean square error (RMSE), average error (AVE), the slope and intercept of major axis linear regression fitted between the observed and predicted values, and Cohen's kappa statistic. Statistical analysis methods are described in the reference publications. Predictive coral distribution maps in 2014-2055 were compared with early local survey reports and reviewed by coral biologists. For more details, see Chen et al., 2020. Parameter or Variable: Uncertainty (calculated); Units: N/A; Observation Category: model output; Sampling Instrument: Models/Analyses > Data Analysis > Environmental Modeling; Sampling and Analyzing Method: Uncertainty of predicted coral density in 2014-2055 were estimated by the standard deviation of 500 simulated predicted spatial density distributions generated with 500 sets of posterior samples drawn from the posterior distributions of estimated parameters. For more details, see Chen et al., 2020.; Data Quality Method: Uncertainty maps were reviewed by subject matter experts. Analysis method was described in and supported by reference publications. For more details, see Chen et al., 2020. asNeeded Metadata are developed, maintained and distributed by NCEI. Updates are performed as needed to maintain currentness. NOAA National Centers for Environmental Information custodian LIDAR LIDAR Light Detection and Ranging Remote sensing instrument with laser and receiver that uses time, strength and polarization of return to determine characteristics of target.